I think this specialization of data teams into 99 different roles (data scientist, data engineer, analytics engineer, ML engineer etc) is generally a bad thing driven by the fact that tools are bad and too hard to use
Show this thread
-
The boundary between different DS roles is pretty thin, and I think that’s good. However, I see a lot of benefit in only trying to apply one type of problem solving lens at a time. Job title is a way to formalize that for longer periods, but it could be a project by project thing
But IMO fragmentation can be a good thing if it’s about clarifying responsibilities. Analysts and scientists may have overlapping skills, but focus their effort on creating value from data in different waysIt’s hard to develop significant expertise in being a data engineer, data analyst and data scientist when you’re trying to learn all of them at once
-
-
100%! my point is that it is hard because all current infra is waaaay too complex and with better infra I would hope roles would be more blurry and collapse. Until then I totally understand why we have such fragmentation
-
Yeah, I think we actually agree here :) Tooling is a source of a lot of noise, and people focus on them as a solution rather than a symptom of the underlying problem
End of conversation
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.